Guest essay by Eric Worrall
Scientists have created a RCP8.5 based hydrological model of changes they expect to the distribution of Malaria in Africa. But they admit their model cannot account for artificial changes like dams and irrigation networks.
Malaria: new map shows which areas will be at risk because of global warming
August 29, 2020 12.49am AEST
Associate Professor in Water Research, University of Leeds
Global Professor in Water & Planetary Health, University of Lincoln
Of an estimated 228 million cases of malaria worldwide each year, around 93% are in Africa. This proportion is more or less the same for the 405,000 malaria deaths globally.
That’s why there are huge efforts underway to provide detailed maps of current malaria cases in Africa, and to predict which areas will become more susceptible in future, since such maps are vital to control and treat transmission. Mosquito populations can respond quickly to climate change, so it is also important to understand what global warming means for malaria risk across the continent.
We have just published a new set of maps in Nature Communications giving the most accurate picture yet of where in Africa will – and won’t – become climatically suitable for malaria transmission.
The malaria parasite thrives where it is warm and wet. Air temperature controls several parts of the transmission cycle, including the mosquito lifespan and rates of development and biting.
If it is too warm or too cold then either the malaria parasite or the mosquito that transmits the parasite between humans will not survive. This suitable temperature range is relatively well established by field and laboratory studies and forms the basis for current projections of the impact of climate change on malaria.
…Read more: https://theconversation.com/malaria-new-map-shows-which-areas-will-be-at-risk-because-of-global-warming-144783
The abstract of the study;
Incorporating hydrology into climate suitability models changes projections of malaria transmission in Africa
Continental-scale models of malaria climate suitability typically couple well-established temperature-response models with basic estimates of vector habitat availability using rainfall as a proxy. Here we show that across continental Africa, the estimated geographic range of climatic suitability for malaria transmission is more sensitive to the precipitation threshold than the thermal response curve applied. To address this problem we use downscaled daily climate predictions from seven GCMs to run a continental-scale hydrological model for a process-based representation of mosquito breeding habitat availability. A more complex pattern of malaria suitability emerges as water is routed through drainage networks and river corridors serve as year-round transmission foci. The estimated hydro-climatically suitable area for stable malaria transmission is smaller than previous models suggest and shows only a very small increase in state-of-the-art future climate scenarios. However, bigger geographical shifts are observed than with most rainfall threshold models and the pattern of that shift is very different when using a hydrological model to estimate surface water availability for vector breeding.Read more: https://www.nature.com/articles/s41467-020-18239-5
To their credit the scientists noted that Malaria can thrive in a wide range of temperatures, so this isn’t just another warming = malaria study.
My main concern is I don’t think the study is useful as guide to policy, I doubt it is applicable to the real world. The study is based on RCP8.5, which is vanishingly unlikely to occur, and the study uses fine grained model hydrological projections.
Given climate models disagree wildly with each other and with observations about the extent of global cloud cover, it is difficult to see how you can infer anything useful from current climate model projections of future rainfall patterns.
Below is a diagram from Pat Frank’s paper, Propagation of Error and the Reliability of Global Air Temperature Projections, showing how wildly wrong climate models are about global cloud cover, when projections are compared to observations. Not only are models wrong, they don’t even agree with each other. Not a good basis for reliable hydrological projections, or any kind of projection for that matter.